
'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :     Optional[int]       =	logging.get_logger(__name__)

lowerCAmelCase_     :     Tuple       =	{
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}







class       __lowerCAmelCase       (    _UpperCAmelCase							):
     snake_case  :       List[str]				 = 'transfo-xl'
     snake_case  :       Tuple				 = ['mems']
     snake_case  :       List[Any]				 = {
         'n_token': 'vocab_size',
         'hidden_size': 'd_model',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }



     def __init__(self		,      lowerCAmelCase__=2_6_7_7_3_5		,      lowerCAmelCase__=[2_0_0_0_0, 4_0_0_0_0, 2_0_0_0_0_0]		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=1_0_2_4		,      lowerCAmelCase__=1_6		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__=4_0_9_6		,      lowerCAmelCase__=4		,      lowerCAmelCase__=False		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=1_6_0_0		,      lowerCAmelCase__=1_0_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=True		,      lowerCAmelCase__=0		,      lowerCAmelCase__=-1		,      lowerCAmelCase__=True		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=True		,      lowerCAmelCase__="normal"		,      lowerCAmelCase__=0.0_1		,      lowerCAmelCase__=0.0_1		,      lowerCAmelCase__=0.0_2		,      lowerCAmelCase__=1e-5		,      lowerCAmelCase__=0		,      **lowerCAmelCase__		,      ):
          _UpperCAmelCase   :    Optional[int]			    =       vocab_size
          _UpperCAmelCase   :    Dict			    =       []
          self.cutoffs.extend(__UpperCamelCase      )
          if proj_share_all_but_first:
               _UpperCAmelCase   :    List[Any]			    =       [False] + [True] * len(self.cutoffs      )
          else:
               _UpperCAmelCase   :    Optional[Any]			    =       [False] + [False] * len(self.cutoffs      )
          _UpperCAmelCase   :    Any			    =       d_model
          _UpperCAmelCase   :    str			    =       d_embed
          _UpperCAmelCase   :    Any			    =       d_head
          _UpperCAmelCase   :    Any			    =       d_inner
          _UpperCAmelCase   :    Optional[int]			    =       div_val
          _UpperCAmelCase   :    Optional[Any]			    =       pre_lnorm
          _UpperCAmelCase   :    Optional[Any]			    =       n_layer
          _UpperCAmelCase   :    Dict			    =       n_head
          _UpperCAmelCase   :    Tuple			    =       mem_len
          _UpperCAmelCase   :    Optional[int]			    =       same_length
          _UpperCAmelCase   :    Tuple			    =       attn_type
          _UpperCAmelCase   :    str			    =       clamp_len
          _UpperCAmelCase   :    Optional[Any]			    =       sample_softmax
          _UpperCAmelCase   :    Dict			    =       adaptive
          _UpperCAmelCase   :    str			    =       dropout
          _UpperCAmelCase   :    str			    =       dropatt
          _UpperCAmelCase   :    Union[str, Any]			    =       untie_r
          _UpperCAmelCase   :    List[Any]			    =       init
          _UpperCAmelCase   :    Any			    =       init_range
          _UpperCAmelCase   :    List[str]			    =       proj_init_std
          _UpperCAmelCase   :    Union[str, Any]			    =       init_std
          _UpperCAmelCase   :    int			    =       layer_norm_epsilon
          super().__init__(eos_token_id=__UpperCamelCase		,      **__UpperCamelCase      )



     @property
     def        snake_case_					(self      ):
          logger.info(F"The model {self.model_type} is one of the few models that has no sequence length limit."      )
          return -1



     @max_position_embeddings.setter
     def        snake_case_					(self		,      lowerCAmelCase__      ):
          raise NotImplementedError(
              F"The model {self.model_type} is one of the few models that has no sequence length limit."      )




"""simple docstring"""



import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer




class 		a_    :







    def __init__(      self      :							Any  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							Dict=13  ,       __UpperCamelCase      :							Tuple=7  ,       __UpperCamelCase      :							List[Any]=True  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							Union[str, Any]=False  ,       __UpperCamelCase      :							List[str]=True  ,       __UpperCamelCase      :							str=99  ,       __UpperCamelCase      :							Optional[Any]=32  ,       __UpperCamelCase      :							Union[str, Any]=5  ,       __UpperCamelCase      :							Tuple=4  ,       __UpperCamelCase      :							Any=37  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							List[str]=5_12  ,       __UpperCamelCase      :							Union[str, Any]=16  ,       __UpperCamelCase      :							Union[str, Any]=2  ,       __UpperCamelCase      :							Union[str, Any]=0.0_2  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=4  ,       __UpperCamelCase      :							str=None  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = seq_length
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_input_mask
        _UpperCAmelCase        = use_token_type_ids
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = type_sequence_label_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = num_choices
        _UpperCAmelCase        = scope







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.vocab_size  )

        _UpperCAmelCase        = None
        if self.use_input_mask:
            _UpperCAmelCase        = random_attention_mask([self.batch_size, self.seq_length]  )

        _UpperCAmelCase        = None
        if self.use_token_type_ids:
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.type_vocab_size  )

        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.type_sequence_label_size  )
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.seq_length]  ,       self.num_labels  )
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.num_choices  )

        _UpperCAmelCase        = self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def   _snake_case       (      self      :							str  )		->Any:






        '''simple docstring'''






        return LlamaConfig(
            vocab_size=self.vocab_size  ,       hidden_size=self.hidden_size  ,       num_hidden_layers=self.num_hidden_layers  ,       num_attention_heads=self.num_attention_heads  ,       intermediate_size=self.intermediate_size  ,       hidden_act=self.hidden_act  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       max_position_embeddings=self.max_position_embeddings  ,       type_vocab_size=self.type_vocab_size  ,       is_decoder=__UpperCamelCase  ,       initializer_range=self.initializer_range  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Union[str, Any]  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       )
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.seq_length, self.vocab_size)  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Dict  ,       )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = True
        _UpperCAmelCase        = True
        _UpperCAmelCase        = LlamaForCausalLM(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        # first forward pass
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       use_cache=__UpperCamelCase  ,       )
        _UpperCAmelCase        = outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor((self.batch_size, 3)  ,       vocab_size=2  )

        # append to next input_ids and
        _UpperCAmelCase        = torch.cat([input_ids, next_tokens]  ,       dim=-1  )
        _UpperCAmelCase        = torch.cat([input_mask, next_mask]  ,       dim=-1  )

        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]
        _UpperCAmelCase        = model(
            __UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       encoder_hidden_states=__UpperCamelCase  ,       encoder_attention_mask=__UpperCamelCase  ,       past_key_values=__UpperCamelCase  ,       output_hidden_states=__UpperCamelCase  ,       )["""hidden_states"""][0]

        # select random slice
        _UpperCAmelCase        = ids_tensor((1,)  ,       output_from_past.shape[-1]  ).item()
        _UpperCAmelCase        = output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCAmelCase        = output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-3  )  )







    def   _snake_case       (      self      :							Tuple  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        (
            (
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,(
            _UpperCAmelCase
        )     ,
        )        = config_and_inputs
        _UpperCAmelCase        = {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
    a   :					Any			     =					(LlamaForCausalLM,) if is_torch_available() else ()
    a   :					Union[str, Any]			     =					(
        {
            'feature-extraction': LlamaModel,
            'text-classification': LlamaForSequenceClassification,
            'text-generation': LlamaForCausalLM,
            'zero-shot': LlamaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a   :					str			     =					False
    a   :					List[str]			     =					False







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = LlamaModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							Optional[int]  )		->Optional[Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
            _UpperCAmelCase        = type
            self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							Dict  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """single_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor([self.model_tester.batch_size]  ,       self.model_tester.type_sequence_label_size  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    def   _snake_case       (      self      :							List[str]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3
        _UpperCAmelCase        = """multi_label_classification"""
        _UpperCAmelCase        = input_dict["""input_ids"""]
        _UpperCAmelCase        = input_ids.ne(1  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = ids_tensor(
            [self.model_tester.batch_size, config.num_labels]  ,       self.model_tester.type_sequence_label_size  ).to(torch.float  )
        _UpperCAmelCase        = LlamaForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       attention_mask=__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.assertEqual(result.logits.shape  ,       (self.model_tester.batch_size, self.model_tester.num_labels)  )







    @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""  )
    def   _snake_case       (      self      :							Dict  )		->int:






        '''simple docstring'''






        pass







    @parameterized.expand([("""linear""",), ("""dynamic""",)]  )
    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = ids_tensor([1, 10]  ,       config.vocab_size  )
        _UpperCAmelCase        = ids_tensor([1, int(config.max_position_embeddings * 1.5  )]  ,       config.vocab_size  )

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        original_model.to(__UpperCamelCase  )
        original_model.eval()
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = original_model(__UpperCamelCase  ).last_hidden_state

        set_seed(42  )  # Fixed seed at init time so the two models get the same random weights
        _UpperCAmelCase        = {"""type""": scaling_type, """factor""": 1_0.0}
        _UpperCAmelCase        = LlamaModel(__UpperCamelCase  )
        scaled_model.to(__UpperCamelCase  )
        scaled_model.eval()
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state
        _UpperCAmelCase        = scaled_model(__UpperCamelCase  ).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )
        else:
            self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(__UpperCamelCase  ,       __UpperCamelCase  ,       atol=1e-5  )  )







@require_torch
class 		a_    (			unittest.TestCase						):







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor([input_ids]  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Optional[int]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""  )
    @slow
    def   _snake_case       (      self      :							Dict  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )
        # Expected mean on dim = -1
        _UpperCAmelCase        = torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # slicing logits[0, 0, 0:30]
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]  )
        # fmt: on
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )







    @unittest.skip(
        """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""  )
    @slow
    def   _snake_case       (      self      :							str  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  ,       device_map="""auto"""  )
        _UpperCAmelCase        = model(torch.tensor(__UpperCamelCase  )  )

        _UpperCAmelCase        = torch.tensor(
            [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]  ,       dtype=torch.floataa  )
        torch.testing.assert_close(out.mean(-1  )  ,       __UpperCamelCase  ,       atol=1e-2  ,       rtol=1e-2  )
        # fmt: off
        _UpperCAmelCase        = torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]  )
        # fmt: on
        torch.testing.assert_close(out[0, 0, :30]  ,       __UpperCamelCase  ,       atol=1e-5  ,       rtol=1e-5  )







    @unittest.skip("""Model is curently gated"""  )
    @slow
    def   _snake_case       (      self      :							Tuple  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
        _UpperCAmelCase        = """Simply put, the theory of relativity states that """
        _UpperCAmelCase        = LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  )
        _UpperCAmelCase        = tokenizer.encode(__UpperCamelCase  ,       return_tensors="""pt"""  )
        _UpperCAmelCase        = LlamaForCausalLM.from_pretrained(
            """meta-llama/Llama-2-13b-chat-hf"""  ,       device_map="""sequential"""  ,       use_safetensors=__UpperCamelCase  )

        # greedy generation outputs
        _UpperCAmelCase        = model.generate(__UpperCamelCase  ,       max_new_tokens=64  ,       top_p=__UpperCamelCase  ,       temperature=1  ,       do_sample=__UpperCamelCase  )
        _UpperCAmelCase        = tokenizer.decode(generated_ids[0]  ,       skip_special_tokens=__UpperCamelCase  )
        self.assertEqual(__UpperCamelCase  ,       __UpperCamelCase  )